In the algorithmic control of network centric operations, it is important to have efficient means to determine identities of elements within a physical battlespace. Since networks are fundamentally stochastic in nature, the algorithmic calculations must be stochastic as well. Typically, this involves multi sensor fusion and multiple locations of data that have to be fused. Presently, there are many different types of fusion algorithms, but none has emerged as a baseline to be used in all scenarios. Present fusion algorithms are either fast but have outputs with weak levels of confidence, or produce high quality characterizations that take very long times to compute.
Bayesian analysis utilizes statistics weighted by experience. While not ideal for statistical analysis in situations with exact binary outcomes, Bayesian analysis can be very useful in situations where not all the variables are known or defined but in which the final state of those variables is well known and defined. Its usefulness in these latter situations is based on its ability to infer experienced final states and then determine the statistical probability of the preferred state without knowing the statistical probability of the underlying states. Thus, simple stochastic analysis may indicate that state 2 has a 1/25 chance of being the final state of a 25 state system, but if experience shows state 2 occurs 80% of the time, a simplified Bayesian analysis would suggest that state 2 actually has an 80% chance of occurring again. A key element of Bayesian analysis is that prior beliefs must be stated and well-defined because they will greatly affect the outcome of the analysis. Problems with technologies utilizing this method involve the requirement of prior beliefs and experience with which to weight the statistical probabilities, the accuracy and validity of these prior beliefs and experiences—there may be no consistent effort to appreciate the ability of past performance to predict future results, and the end results of the analysis may rely more on the weighting of prior experiences than the actual data itself.
Thus, a heretofore unaddressed need exists in the industry to fuse dynamic sensor data from multiple inputs in real-time.